Official PyTorch implementation of our CVPRW2023 paper: Efficient Multi-exposure Image Fusion via Filter-dominated Fusion and Gradient-driven Unsupervised Learning. Paper link
Frameworks
FFMEF & GIFloss
Results
multi-focus fusion & visible-infrared fusion
We provide a simple training and testing process as follows:
Dependencies
- Python 3.8
- PyTorch 1.10.0+cu113
Train
The datasets samples are placed in images\dataset (including MEFB[1], MFIF[2], VIFB[3], and SICE[4]).
Multi-Exposure Image Fusion (MEF)
python train.py --config 1
Multi-Focus Image Fusion (MFF)
python train.py --config 2
Visible-Infrared Image Fusion (VIF)
python train.py --config 3
Then, the checkpoints and log file are saved in output.
Test
The pretrained models are placed in ckp.
MEF
python test.py --config 1 --ckp mef.pth
MFF
python test.py --config 2 --ckp mff.pth
VIF
python test.py --config 3 --ckp vif.pth
Finally, the fused results can be found in images\fused.
Citation
@inproceedings{zheng2023ffmef,
title={Efficient Multi-exposure Image Fusion via Filter-dominated Fusion and Gradient-driven Unsupervised Learning},
author={Zheng, Kaiwen and Huang, Jie and Yu, Hu and Zhao, Feng},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
year={2023}
}
Reference
[1] Zhang X. Benchmarking and comparing multi-exposure image fusion algorithms[J]. Information Fusion, 2021, 74: 111-131.
[2] Zhang X. Deep learning-based multi-focus image fusion: A survey and a comparative study[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[3] Zhang X, Ye P, Xiao G. VIFB: A visible and infrared image fusion benchmark[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020: 104-105.
[4] Cai J, Gu S, Zhang L. Learning a deep single image contrast enhancer from multi-exposure images[J]. IEEE Transactions on Image Processing, 2018, 27(4): 2049-2062.